Tabular Learning.
Recently there is extensive work [1]}, [2]}, [3]}, [4]}, [5]} being proposed to model on tabular datasets.
However, these methods are usually known for the characteristics of the table's attributes.
Inspired by TransTab [5]}, we seek to use \(\texttt {MLP}\)  to globally model on tabular datasets.
Furthermore, to enhance the modeling capability of the whole model, inductive bias based on convolutional operators is also fused in the network.
The architecture of the whole model is thanks to MLP-Mixer [7]}.
